Markov Switching Models for Volatility: Filtering, Approximation and Duality
25 Pages Posted: 11 Nov 2013
Date Written: November 11, 2013
Abstract
This paper is devoted to show duality in the estimation of Markov Switching (MS) processes for volatility. It is well-known that MS-GARCH models suffer of path dependence which makes the estimation step unfeasible with usual Maximum Likelihood procedure. However, by rewriting the MS-GARCH model in a suitable linear State Space representation, we are able to give a unique framework to reconcile the estimation obtained by the Kalman Filter and with some auxiliary models proposed in the literature. Reasoning in the same way, we present a linear Filter for MS-Stochastic Volatility (MS-SV) models on which different conditioning sets yield more flexibility in the estimation. Estimation on simulated data and on short-term interest rates shows the feasibility of the proposed approach.
Keywords: Markov Switching, MS-GARCH model, MS-SV model, estimation, auxiliary model, Kalman Filter
JEL Classification: C01, C13, C58
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